The pathogenesis of heart failure is complex, involving heterogeneous genetic and environmental factors. Genome-wide association studies (GWAS) have had limited success for heart failure and the genetic factors contributing to human heart failure remain poorly understood. A major challenge in the field is to fully understand the heterogeneity of the disease in order to develop more effective and personalized therapies. In this proposal, we have developed a novel approach by using GWAS across a hybrid mouse diversity panel (HMDP) under a well defined pathological stressor to systematically identify genetic factors and molecular networks implicated in heart failure. We believe this novel approach has several major advantages. First, the HMDP consists of ~100 common inbred and recombinant inbred (RI) strains which have been either entirely sequenced or densely genotyped [over 140,000 single nucleotide polymorphisms (SNPs)]. Second, the insights learnt from mouse models of heart failure should provide relevant guidance for future mechanistic, epidemiological and genetic studies in human. Lastly, Chronic excessive adrenergic overdrive is a well recognized major contributor to human heart failure and understanding the genetic modulators to betaAR signaling would have a major impact. With precisely administered chronic treatment of isoproterenol, a non-selective betaAR agonist, we will be able to quantitatively inflict a pathological insult in a relatively high-throughput manner. Accordingly, we propose to 1). Identify genetic loci in mouse contributing to cardiac responses to chronic beta-adrenergic stimulation by quantitative analysis of cardiac function and remodeling in the HMDP mice in response to chronic isoproterenol stimulation, and association analysis with an efficient mixed model algorithm to identify regions of the genome and potential candidate genes linking to the different features of cardiac response to chronic beta-adrenergic stimulation. 2). Model pathways contributing to regulation of heart function and hypertrophy by global expression array analyses of hearts before and after isoproterenol treatment to map loci contributing to differences in gene expression that are associated with chronic beta-adrenergic response, and construction co-expression networks to identify subnetworks associated with chronic beta-adrenergic response. In short, the systems approach designed in this proposal will bring novel insights to genes and their interacting networks implicated in betaAR signaling and heart failure.